Do Intrabony Defects Have a Worse Clinical Response to Step 2 of Periodontal Therapy and Repeated Subgingival Instrumentation Compared with Suprabony Defects? A Systematic Review
Bibliographic record
Abstract
This study aimed to assess the differential clinical response to step 2 of periodontal therapy and repeated subgingival instrumentation between teeth with suprabony and intrabony defects. Electronic and manual searches were performed to identify studies reporting the differential clinical outcomes of nonsurgical periodontal therapy (NSPT) in the presence or absence of intrabony defects. The Cochrane Risk of Bias 2 and the Newcastle-Ottawa scale were used to assess the risk of bias. A total of 2,348 articles were initially screened, and a total of 5 articles were finally included. Regarding the primary outcome measure, two studies reported probing pocket depth (PPD) reductions at 6 months after step 2 of periodontal therapy, showing an opposite response of intrabony defects compared to suprabony defects (3.2 ± 1.9 mm intrabony vs 2.2 ± 1.7 mm suprabony in one study, and 0.48 ± 0.42 mm intrabony vs 0.72 ± 0.36 mm suprabony in the other), while one study reported no differences at 3 months. One study showed a negative association between the presence of an intrabony defect and PPD reduction at 9 months after nonsurgical step 3 (P < .05). Due to the limited number of studies and heterogeneity of the data, conflicting evidence emerged for the differential response to NSPT of intrabony and suprabony defects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".